--- title: "Running Claude Code with OpenAI Models: A Step-by-Step Setup Guide" description: "Run Claude Code with OpenAI models like GPT-5 and GPT-4o using Bifrost, the open-source AI gateway. Step-by-step setup, zero code changes.\n\nRunning Claude Code with OpenAI models is one of the most common requests from engineering teams that have standardized on Anthropic's terminal-based" image: https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w1200/2026/07/running-claude-code-with-openai-models-a-step-by-step-setup--bifrost-circuit.optimized.png --- Try Bifrost Enterprise free for 14 days. [Request access](https://www.getmaxim.ai/#enterprise-trial) **Run Claude Code with OpenAI models like GPT-5 and GPT-4o using** [**Bifrost**](https://www.getmaxim.ai)**, the open-source AI gateway. Step-by-step setup, zero code changes.** Running Claude Code with OpenAI models is one of the most common requests from engineering teams that have standardized on Anthropic's terminal-based coding agent but need flexibility on the model layer. Claude Code ships locked to Anthropic's API by default, which limits cost optimization, provider redundancy, and benchmarking workflows. [Bifrost](https://www.getmaxim.ai), the high-performance [open-source AI gateway](https://github.com/maximhq/bifrost) by Maxim AI, removes that constraint by translating Claude Code's Anthropic-format requests into OpenAI's Chat Completions format transparently. This guide walks through the complete setup, from installing Bifrost to overriding model tiers to validating tool-call behavior in production. ## Why Run Claude Code with OpenAI Models Claude Code's agentic capabilities (file operations, terminal commands, multi-file refactors) make it the default coding agent for many teams, but production engineering organizations frequently need to route traffic to OpenAI models for several reasons: - **Cost optimization**: GPT-5.2 is currently priced at $1.75 per million input tokens and $14 per million output tokens, which can be lower than Claude Sonnet for input-heavy workloads. - **Model benchmarking**: Comparing GPT-5 family performance against Claude on specific codebases without rewriting agent infrastructure. - **Provider redundancy**: Avoiding hard dependency on a single LLM provider for critical developer workflows. - **Compliance and data residency**: Some organizations require routing LLM traffic through Azure OpenAI for regional or regulatory reasons. - **Specialized model strengths**: Using OpenAI models for agentic terminal tasks where they have benchmark leads on Terminal-Bench style workloads. Without an [AI gateway](https://www.getmaxim.ai/articles/top-5-llm-gateways-in-2026-a-production-ready-comparison/), none of these are possible. Claude Code talks Anthropic's Messages API exclusively, and OpenAI's API speaks a different format. Bifrost solves this by acting as a fully Anthropic-compatible endpoint that translates requests on the fly to whichever provider you target. ## How Bifrost Routes Claude Code Traffic to OpenAI [Bifrost](https://www.getmaxim.ai) sits between Claude Code and the upstream LLM providers as a high-performance proxy. The routing flow works as follows: - Claude Code sends an Anthropic Messages API request to Bifrost instead of `api.anthropic.com`. - Bifrost intercepts the request, parses the model identifier (for example, `openai/gpt-5`), and translates the request schema to OpenAI's Chat Completions format. - The translated request is dispatched to OpenAI's endpoint with the configured API key. - OpenAI's response is translated back to Anthropic's response schema and returned to Claude Code. - Claude Code processes the response as if it came from Anthropic directly. This translation happens at runtime with only 11 microseconds of overhead per request at 5,000 requests per second in sustained benchmarks. Bifrost publishes [independent performance benchmarks](https://www.getmaxim.ai/resources/benchmarks) covering latency and throughput across the supported provider matrix. Bifrost's [drop-in replacement](https://docs.getbifrost.ai/features/drop-in-replacement) architecture is the foundation that makes this work. Claude Code only needs a base URL change; everything else stays identical. ## Step 1: Install and Start Bifrost Bifrost runs as a local gateway that Claude Code connects to over HTTP. The fastest path to a running instance is the NPX installer. ```bash npx -y @maximhq/bifrost ``` This command starts Bifrost on `http://localhost:8080` with a built-in web UI for provider configuration, request logs, and real-time monitoring. The gateway is zero-config: no YAML files, no environment scaffolding required to start. For production deployments, Docker is the recommended path: ```bash docker run -p 8080:8080 -v $(pwd)/data:/app/data maximhq/bifrost ``` Bifrost also supports [Kubernetes deployment](https://docs.getbifrost.ai/deployment-guides/k8s) for teams running it as shared infrastructure across engineering organizations. ## Step 2: Configure OpenAI as a Provider Once Bifrost is running, open the web UI at `http://localhost:8080` and add OpenAI as a provider. You will need: - An OpenAI API key with access to the models you plan to use (GPT-5, GPT-4o, GPT-4o-mini, etc.) - A provider key name for tracking and rotation purposes You can configure providers either through the UI, the [provider configuration API](https://docs.getbifrost.ai/quickstart/gateway/provider-configuration), or a `config.json` file. For a config-file approach, the structure looks like this: ```json { "providers": { "openai": { "keys": [ { "name": "openai-primary", "value": "env.OPENAI_API_KEY", "models": [], "weight": 1.0 } ] } } } ``` Bifrost reads `env.OPENAI_API_KEY` from the environment, which keeps the actual key out of the config file. For enterprise deployments, integration with [HashiCorp Vault, AWS Secrets Manager, and similar systems](https://docs.getbifrost.ai/enterprise/secret-management) replaces direct environment variable usage. ## Step 3: Point Claude Code at Bifrost With Bifrost running and OpenAI configured, redirect Claude Code to the gateway by setting two environment variables: ```bash export ANTHROPIC_API_KEY=your-bifrost-virtual-key export ANTHROPIC_BASE_URL=http://localhost:8080/anthropic ``` If virtual keys are disabled in your Bifrost instance, set `ANTHROPIC_API_KEY` to `dummy`. With [virtual keys](https://docs.getbifrost.ai/features/governance/virtual-keys) enabled, this value becomes your governance handle: per-key budgets, rate limits, and tool filtering all anchor on it. After exporting these variables, run `claude` in a new terminal session. Claude Code now routes every request through Bifrost. ## Step 4: Override Model Tiers to Use OpenAI Models Claude Code uses three model tiers internally: Sonnet (default for most tasks), Opus (complex reasoning), and Haiku (fast, lightweight operations). Each tier can be independently overridden using environment variables: ```bash # Replace the Sonnet tier with GPT-5 for primary coding tasks export ANTHROPIC_DEFAULT_SONNET_MODEL="openai/gpt-5" # Replace the Opus tier with GPT-4o for complex reasoning export ANTHROPIC_DEFAULT_OPUS_MODEL="openai/gpt-4o" # Keep Haiku on Anthropic for speed-sensitive operations export ANTHROPIC_DEFAULT_HAIKU_MODEL="anthropic/claude-haiku-4-5-20251001" ``` The `provider/model` format tells Bifrost which provider and model to route to. Any provider configured in your Bifrost instance is available here, including [20+ supported providers](https://docs.getbifrost.ai/providers/supported-providers/overview) such as Anthropic, AWS Bedrock, Google Vertex AI, Azure OpenAI, Groq, Mistral, Cohere, and Ollama for local inference. For full OpenAI routing, override all three tiers: ```bash export ANTHROPIC_DEFAULT_SONNET_MODEL="openai/gpt-5" export ANTHROPIC_DEFAULT_OPUS_MODEL="openai/gpt-4o" export ANTHROPIC_DEFAULT_HAIKU_MODEL="openai/gpt-4o-mini" ``` You can also launch Claude Code with a specific model using the `--model` flag, or switch models mid-session using `/model openai/gpt-5` inside Claude Code itself. The switch is instantaneous and preserves conversation context. ## Step 5: Validate Tool-Call Behavior This step is the most important and the most frequently skipped. Claude Code depends heavily on tool calling for file operations, terminal commands, and code editing. Not every model and not every provider streams tool call arguments correctly. Before treating a configuration as production-ready, run a tool-heavy task and verify: - File reads and writes complete successfully. - Multi-step terminal commands execute without empty argument errors. - Long-running edits maintain conversation context across turns. OpenAI's GPT-5 and GPT-4o models support tool calling natively and work reliably with Claude Code. Some aggregator-style providers do not stream function-call arguments correctly, which causes Claude Code to fail silently on file operations. If a model passes basic chat tests but fails on file edits, the most likely cause is incomplete tool-call streaming on the upstream provider. In that case, switch to a different provider in your Bifrost configuration. ## Step 6: Add Failover, Caching, and Governance Once Claude Code is routing through Bifrost, the full set of gateway features becomes available without additional configuration on the Claude Code side: - **Automatic failover**: Configure [fallback chains](https://docs.getbifrost.ai/features/fallbacks) so that if OpenAI hits a rate limit or experiences an outage, traffic shifts to Anthropic, Bedrock, or Vertex without dropping the session. - **Semantic caching**: Bifrost's [semantic caching](https://docs.getbifrost.ai/features/semantic-caching) reduces token spend for semantically similar requests, which compounds quickly across a team using Claude Code daily. - **Virtual keys and budgets**: [Virtual keys](https://docs.getbifrost.ai/features/governance/virtual-keys) enforce per-developer or per-team budgets, rate limits, and model access policies. Engineering managers gain hierarchical cost control across teams and customers. - **Observability**: Bifrost exports OpenTelemetry traces and Prometheus metrics natively, so every Claude Code request is captured in Grafana, New Relic, or Datadog dashboards. - **Guardrails**: For regulated environments, [enterprise guardrails](https://docs.getbifrost.ai/enterprise/guardrails) integrate AWS Bedrock Guardrails, Azure Content Safety, and Patronus AI to enforce policy on Claude Code traffic. For teams managing Claude Code at scale, the [Claude Code integration page](https://www.getmaxim.ai/resources/claude-code) covers the full configuration matrix including AWS Bedrock passthrough and Google Vertex AI authentication. ## Persistent Configuration for Daily Workflows Adding the Bifrost environment variables to a single shell session works for testing. For persistent use, append the exports to `~/.bashrc`, `~/.zshrc`, or your shell's equivalent: ```bash # Bifrost gateway export ANTHROPIC_API_KEY=your-bifrost-virtual-key export ANTHROPIC_BASE_URL=http://localhost:8080/anthropic # Model tier overrides export ANTHROPIC_DEFAULT_SONNET_MODEL="openai/gpt-5" export ANTHROPIC_DEFAULT_OPUS_MODEL="openai/gpt-4o" export ANTHROPIC_DEFAULT_HAIKU_MODEL="openai/gpt-4o-mini" ``` If you prefer running Claude Code inside VS Code, install the Claude Code extension and add the same variables to its settings. Bifrost handles every Claude Code surface (CLI, VS Code, JetBrains) identically because it operates at the protocol layer. ## Get Started Running Claude Code with OpenAI Models Routing Claude Code through Bifrost takes a few minutes and unlocks the full multi-provider model flexibility that engineering teams need in 2026. Once configured, you can run Claude Code with OpenAI models, Anthropic models, Bedrock-hosted Claude, Vertex Gemini, Groq-hosted open-weights, or local Ollama, all from the same Claude Code interface, with a single environment variable controlling the active model. To see how Bifrost handles enterprise Claude Code deployments at scale, including clustering, RBAC, in-VPC deployments, and audit logging, [book a demo with the Bifrost team](https://www.getmaxim.ai/book-a-demo) or explore the [Bifrost GitHub repository](https://github.com/maximhq/bifrost) to start running Claude Code with OpenAI models today. ## Read next [![Claude Agent SDK Through a Gateway: Routing, Budgets, MCP](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/10/claude-agent-sdk-through-a-gateway-routing-budgets-mcp-bifrost-1920x1080.png) The Claude Agent SDK packages Claude Code's agent loop, tools, and context management as a Python and TypeScript library. This guide shows how to route Claude Agent SDK traffic through Bifrost for provider routing and failover, per-tenant budgets, and governed MCP tool access.](https://www.getmaxim.ai/articles/claude-agent-sdk-through-a-gateway-routing-budgets-mcp/) [![OpenAI Responses API vs Chat Completions vs Anthropic Messages](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/10/openai-responses-api-vs-chat-completions-vs-anthropic-messag-bifrost-1920x1080.png) The OpenAI Responses API is OpenAI's stateful, item-based interface for agentic apps, while Chat Completions and Anthropic Messages remain stateless message APIs. This guide compares the three on state, tools, reasoning, and retention, and shows how Bifrost serves all three formats.](https://www.getmaxim.ai/articles/openai-responses-api-vs-chat-completions-vs-anthropic-messages/) [![FinOps for AI: Showback and Chargeback for LLM Spend](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/10/finops-for-ai-showback-and-chargeback-for-llm-spend-bifrost-1920x1080.png) FinOps for AI is the practice of making LLM and agent spend visible, attributable, and accountable to the teams that create it. This guide explains showback vs chargeback for AI, the attribution model AI spend needs, and how to build both from request-level data at the AI gateway.](https://www.getmaxim.ai/articles/finops-for-ai-showback-and-chargeback-for-llm-spend/) ## More in AI Gateway [![Top 5 AI Gateways for Enterprises in 2026](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/07/top-5-ai-gateways-for-enterprises-in-2026-bifrost-paper-waves.optimized.png) Compare the top 5 AI gateways for enterprises in 2026 across performance, governance, MCP support, and production readiness. Bifrost is the best choice for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. TL;DR \* An enterprise-ready AI gateway is judged on seven](https://www.getmaxim.ai/articles/top-5-ai-gateways-for-enterprises-in-2026/) [![Top 5 AI Guardrails Platforms for Responsible Enterprise AI in 2026](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/07/top-5-ai-guardrails-platforms-for-responsible-enterprise-ai--bifrost-circuit.optimized.png) Compare the top 5 AI guardrails platforms for enterprise responsible AI: gateway-level enforcement, content safety, PII detection, and compliance evidence. AI guardrails platforms have become the enforcement layer that determines whether an enterprise AI deployment is defensible to auditors, regulators, and customers. With the EU AI Act's](https://www.getmaxim.ai/articles/top-5-ai-guardrails-platforms-for-responsible-enterprise-ai-in-2026/) ```json { "@context": "https://schema.org", "@type": "Article", "publisher": { "@type": "Organization", "name": "Maxim Articles", "url": "https://www.getmaxim.ai/articles/", "logo": { "@type": "ImageObject", "url": "https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w256h256/2025/08/thumbnail.png", "width": 60, "height": 60 } }, "author": { "@type": "Person", "name": "Kuldeep Paul", "image": { "@type": "ImageObject", "url": "https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/2025/08/1727978381919.jpeg", "width": 800, "height": 800 }, "url": "https://www.getmaxim.ai/articles/author/kuldeep/", "sameAs": [] }, "headline": "Running Claude Code with OpenAI Models: A Step-by-Step Setup Guide", "url": "https://www.getmaxim.ai/articles/running-claude-code-with-openai-models-a-step-by-step-setup-guide/", "datePublished": "2026-04-24T03:40:00.000Z", "dateModified": "2026-10-16T06:14:38.000Z", "image": { "@type": "ImageObject", "url": "https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w1200/2026/07/running-claude-code-with-openai-models-a-step-by-step-setup--bifrost-circuit.optimized.png", "width": 1200, "height": 630 }, "keywords": "AI Gateway", "description": "Run Claude Code with OpenAI models like GPT-5 and GPT-4o using Bifrost, the open-source AI gateway. 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